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Millimeter wave (mmWave) communications have a broad spectrum and can supportdata rates in the order of gigabits per second, as envisioned in 5G systems.However, they cannot be used for long distances due to their sensitivity toattenuation loss. To enable their use in the 5G network, it requires that thetransmission energy be focused in sharp pencil beams. As any misalignmentbetween the transmitter and receiver beam pair can reduce the data ratesignificantly, it is important that they are aligned as much as possible. Tofind the best transmit-receive beam pair, recent beam alignment (BA) techniquesexamine the entire beam space, which might result in a large amount of BAlatency. Recent works propose to adaptively select the beams such that thecumulative reward measured in terms of received signal strength or throughputis maximized. In this paper, we develop an algorithm that exploits the unimodalstructure of the received signal strengths of the beams to identify the bestbeam in a finite time using pure exploration strategies. Strategies thatidentify the best beam in a fixed time slot are more suitable for wirelessnetwork protocol design than cumulative reward maximization strategies thatcontinuously perform exploration and exploitation. Our algorithm is namedUnimodal Bandit for Best Beam (UB3) and identifies the best beam with a highprobability in a few rounds. We prove that the error exponent in theprobability does not depend on the number of beams and show that this is indeedthe case by establishing a lower bound for the unimodal bandits. We demonstratethat UB3 outperforms the state-of-the-art algorithms through extensivesimulations. Moreover, our algorithm is simple to implement and has lowercomputational complexity.

UB3: Best Beam Identification in Millimeter Wave Systems via Pure Exploration Unimodal Bandits